Actions for selected content:

Send content to

To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .

To send content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services
Please confirm that you accept the terms of use.

The use of metabolic profiles is widely accepted as an assessment method of the dairy cows’ energy status (Whitaker, 2004). Three of the most reliable parameters that can be used are serum concentration of glucose, beta hydroxybutyric acid (BHB) and non-esterified fatty acids (NEFA). There is growing interest in the changes of these parameters during lactation (Wathes et al., 2007). The scope of this study was to record and characterise the fluctuation of serum levels of glucose, BHB and NEFA during a 305-day lactation of primiparous Holstein cows raised under commercial conditions.

This study investigated the profile of locomotion score and lameness before the first calving and throughout the first (n=237) and second (n=66) lactation of 303 Holstein cows raised on a commercial farm. Weekly heritability estimates of locomotion score and lameness, and their genetic and phenotypic correlations with milk yield, body condition score, BW and reproduction traits were derived. Daughter future locomotion score and lameness predictions from their sires’ breeding values for conformation traits were also calculated. First-lactation cows were monitored weekly from 6 weeks before calving to the end of lactation. Second-lactation cows were monitored weekly throughout lactation. Cows were locomotion scored on a scale from one (sound) to five (severely lame); a score greater than or equal to two defined presence of lameness. Cows’ weekly body condition score and BW was also recorded. These records were matched to corresponding milk yield records, where the latter were 7-day averages on the week of inspection. The total number of repeated records amounted to 12 221. Data were also matched to the farm’s reproduction database, from which five traits were derived. Statistical analyses were based on uni- and bivariate random regression models. The profile analysis showed that locomotion and lameness problems in first lactation were fewer before and immediately after calving, and increased as lactation progressed. The profile of the two traits remained relatively constant across the second lactation. Highest heritability estimates were observed in the weeks before first calving (0.66 for locomotion score and 0.54 for lameness). Statistically significant genetic correlations were found for first lactation weekly locomotion score and lameness with body condition score, ranging from −0.31 to −0.65 and from −0.44 to −0.76, respectively, suggesting that cows genetically pre-disposed for high body condition score have fewer locomotion and lameness issues. Negative (favourable) phenotypic correlations between first lactation weekly locomotion score/lameness and milk yield averaged −0.27 and −0.17, respectively, and were attributed to management factors. Also a phenotypic correlation between lameness and conception rate of −0.19 indicated that lame cows were associated with lower success at conceiving. First-lactation daughter locomotion score and/or lameness predictions from sires’ estimated breeding values for conformation traits revealed a significant linear effect of rear leg side view, rear leg rear view, overall conformation, body condition score and locomotion, and a quadratic effect of foot angle.

This simulation study investigated the farm-level economic benefits of a genetic improvement scheme using artificial insemination (AI) with fresh ram semen in dairy sheep of the Chios breed in Greece. Data were collected from 67 farms associated with the Chios Sheep Breeders’ Cooperative ‘Macedonia’, describing the percentage of ewes that would be artificially inseminated in the flock, pregnancy rate, annual ram costs that could be saved using AI rather than natural mating, expected improvement in milk production, annual costs of semen and feed, milk price and number of years of AI usage. The study considered 77 760 possible scenarios in a 3 × 4 × 4 × 3 × 3 × 3 × 4 × 15 factorial arrangement. Analysis of variance was used to investigate the effect of each factor on farm profitability. All factors considered were statistically significant (P < 0.001), but their effect varied. The number of years using AI had the greatest effect on profitability and farmers should become aware that using AI is a long-term investment. Semen price, pregnancy rate and improvement in milk production also had substantial effects. The price of milk and feed had a considerably lower effect on profitability, as did the annual cost of maintaining rams that would be replaced by AI. A positive annual and cumulative return was achieved in the model within the first 6 years. The cost of semen was estimated at 8€ to 10€ per dose for the first 5 years. Where the annual improvement in milk production was 1% of annual phenotypic mean (e.g. 3.0 kg) profitability of the scheme was improved greatly.

Declining reproductive performance is a major problem for the global dairy industry (Lucy 2001) whereas magnitude and duration of postpartum negative energy balance of dairy cows are considered as the main reasons (de Vries and Veerkamp 2000). Moreover, various energy balance indicators, such as body condition score (BCS) and plasma βhydroxybutyrate (BHB) concentration, have been correlated with reduced reproductive performance (Pryce et al 2001, Taylor et al 2003, Patton et al 2007, Walsh et al 2007). Such information has been already used to adjust herd management practices in order to prevent negative effects on reproduction. Furthermore, the ability to predict reproductive performance of cows with reasonable accuracy would also be very useful to dairy farmers. In such case, important management decisions (e.g. length of voluntary waiting period, starting dates of synchronisation programs and price of semen used), could be made for each individual cow. The aim of this study was to investigate whether combining certain energy balance indicators would yield useful predictions of cow reproductive performance at 1st artificial insemination (AI).

Work on lameness has been focused on meat sheep but there is limited information on dairy sheep. Lameness is a welfare problem, which reduces productivity and it is a major problem in most sheep keeping countries (Winter, 2008). The latter is important for Greece which is ranked second (after Italy) in milk sheep production in Europe (deRancourt et al. 2006). The objective of this study was twofold. Firstly, to characterise the farming system in a representative sample of dairy sheep flocks, and to categorise them in certain clusters in relation to predisposing factors of lameness. Secondly, to assess the prevalence and the major epidemiological characteristics of lameness.

Dairy sheep production is an important industry in Greece and other Mediterranean countries. The traditional system of lambing once a year, as well as the size of the flocks and the extended lactation period has been an obstacle to implement accelerated breeding systems that could capitalise on the full potential of the ewe (Fahmy and Lavallee, 1990). The objective of this study was to develop a management system which combines an accelerated breeding scheme and grouping of ewes in relation to their milk yield, for large, intensively reared, dairy flocks.

Reproductive efficiency in the dairy herd is the most important factor for its economic success and a major concern for dairy farmers when using artificial insemination (AI) or natural service (NS). Our objectives were to estimate, compare and analyse the costs associated with breeding cattle by do-it-yourself (DIY) AI and NS and identify the factors that influence them, under typical dairy farming conditions in Greece. A simulation study was designed based on data from 120 dairy cattle farms that differed in size (range 40 to 285 cows) and milk production level (4000 to 9300 kg per cow per year). Different scenarios were employed to estimate costs associated directly with AI and NS as well as potentially extended calving intervals (ECI) due to AI. Results showed that bull maintenance costs for NS were €1440 to €1670 per year ($1,820 to $2,111). Direct AI costs were higher than those for NS for farms with more than 30 cows and ECI constituted a considerable additional burden. In fact, amongst the factors that affected the amount of milk needed to cover total extra AI costs, number of days open was the dominant one. Semen, feed and heifer prices had a very small effect. When, hypothetically, use of NS bulls results in a calving interval of 12 months, AI daughters with a calving interval of 13.5 months have to produce about 705 kg of additional milk in order to cover the extra cost. Their actual milk production, however, exceeds this limit by more than 25%. When real calving intervals are considered (13.0 v. 13.7 months for NS and AI, respectively) AI daughters turn out to produce more than twice the additional amount of milk needed. It was concluded that even under less than average management conditions, AI is more profitable than the best NS scenario. The efficient communication of this message should be a primary concern of the AI industry.

Recommend this

Email your librarian or administrator to recommend adding this to your organisation's collection.